where \(\alpha _0\)  is the model intercept and \(\mathbf {\alpha _1}\)  is a vector of model coefficients. While most applications of weighting, e.g. to control for confounding in observational studies, construct “inverse probability” weights for treatment assignment, MAIC uses “odds weighting” [1]}, [2]} to model trial assignment. The weight \(w_i\)  represents the conditional odds that an individual \(i\)  with covariates \(\mathbf {z}_i\) , selected as marginal effect modifiers, is enrolled in the competitor study. Alternatively, the weight represents the inverse conditional odds that the individual is enrolled in the index study.
